Instructions to use Sakeador/BertUn55 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sakeador/BertUn55 with BERTopic:

from bertopic import BERTopic

model = BERTopic.load("Sakeador/BertUn55")

llama-cpp-python

How to use Sakeador/BertUn55 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Sakeador/BertUn55",
	filename="BertUn55-V.1.3-70B.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Sakeador/BertUn55 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Sakeador/BertUn55
# Run inference directly in the terminal:
llama-cli -hf Sakeador/BertUn55

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Sakeador/BertUn55
# Run inference directly in the terminal:
llama-cli -hf Sakeador/BertUn55

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Sakeador/BertUn55
# Run inference directly in the terminal:
./llama-cli -hf Sakeador/BertUn55

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Sakeador/BertUn55
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Sakeador/BertUn55

Use Docker

docker model run hf.co/Sakeador/BertUn55

LM Studio
Jan
Ollama
How to use Sakeador/BertUn55 with Ollama:
```
ollama run hf.co/Sakeador/BertUn55
```

Unsloth Studio new

How to use Sakeador/BertUn55 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Sakeador/BertUn55 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Sakeador/BertUn55 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Sakeador/BertUn55 to start chatting

Docker Model Runner
How to use Sakeador/BertUn55 with Docker Model Runner:
```
docker model run hf.co/Sakeador/BertUn55
```

Lemonade

How to use Sakeador/BertUn55 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Sakeador/BertUn55

Run and chat with the model

lemonade run user.BertUn55-{{QUANT_TAG}}

List all available models

lemonade list

A newer version of this model is available: meta-llama/Llama-3.3-70B-Instruct

Special Acknowledgments

"This model was created as a tribute to an exceptional individual whose unwavering support has been pivotal throughout my technology career. Thank you for being my mentor, inspiration, and anchor through every professional challenge." 🔗🧠

Dedicated to: [XSecretNameX]
Key Contributions:

Model architecture guidance
Critical code debugging
Pipeline optimization

Development Background

This project was developed in recognition of professional support received during:

Cloud infrastructure migration (AWS/GCP)
MLOps implementation
High-scale system troubleshooting (2020-2024)

Collaboration Highlights

This architecture incorporates lessons learned from collaborative work on:

CI/CD pipeline design
Kubernetes cluster management
Real-time monitoring systems

Downloads last month: 5

GGUF

Model size

71B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

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Model tree for Sakeador/BertUn55

Base model

mistralai/Codestral-22B-v0.1

Quantized

(50)

this model

Sakeador
/

BertUn55

Special Acknowledgments

Development Background

Collaboration Highlights

Model tree for Sakeador/BertUn55

Datasets used to train Sakeador/BertUn55